This dataset consists of approximately 312K \((Q, \mathcal {D}, Y)\)  triples [1]}, and is developed using the CNN / Daily Mail (DM) dataset which is commonly used for single-document summarization [2]}.
Each \((Q, \mathcal {D}, Y)\)  triple is generated from a news article. \(Q\)  is the title of the article, \(Y\)  is the single-document summary of the article provided in the original CNN/DM dataset. \(\mathcal {D}\)  consists of documents generated using two methods. First, each news article is chunked into around 20 small paragraphs, and each paragraph is treated as \(D \in \mathcal {D}\)  if it is part of the news article. Second, a BM25 search engine is used to retrieve for each query (or title) top-4 paragraphs from all the paragraphs that do not belong to the news article.
The authors argue that QMDS-CNN is a better dataset for QMDS research than WikiSum because (1) summaries are of high-quality because they are manually generated, and are query-focused since they reflect the title; and (2) each summary contains information from multiple documents by design.
The only main downsize is that the queries are not real but simulated by titles. The simulation might not be desirable since titles are generated by the authors who also create the documents while queries are issued by users who have no idea about the documents.
